Overview

Dataset statistics

Number of variables51
Number of observations227
Missing cells316
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.6 KiB
Average record size in memory408.6 B

Variable types

Boolean27
Numeric15
Categorical9

Alerts

patient id has a high cardinality: 227 distinct values High cardinality
years is highly correlated with pack yearsHigh correlation
triglycmg/dl is highly correlated with chol/hd ratioHigh correlation
chol/hd ratio is highly correlated with triglycmg/dl and 1 other fieldsHigh correlation
bmi is highly correlated with mass/kgHigh correlation
hdl mg/dl is highly correlated with chol/hd ratioHigh correlation
height/m is highly correlated with mass/kgHigh correlation
mass/kg is highly correlated with bmi and 1 other fieldsHigh correlation
pack years is highly correlated with yearsHigh correlation
years is highly correlated with pack yearsHigh correlation
triglycmg/dl is highly correlated with chol/hd ratioHigh correlation
chol/hd ratio is highly correlated with triglycmg/dl and 1 other fieldsHigh correlation
bmi is highly correlated with mass/kgHigh correlation
hdl mg/dl is highly correlated with chol/hd ratioHigh correlation
height/m is highly correlated with mass/kgHigh correlation
mass/kg is highly correlated with bmi and 1 other fieldsHigh correlation
pack years is highly correlated with yearsHigh correlation
years is highly correlated with pack yearsHigh correlation
bmi is highly correlated with mass/kgHigh correlation
mass/kg is highly correlated with bmiHigh correlation
pack years is highly correlated with yearsHigh correlation
study is highly correlated with dm patient medical history and 2 other fieldsHigh correlation
neuropathy autonomic symptoms is highly correlated with numbness autonomic symptomsHigh correlation
hyperlipidemia patient medical history is highly correlated with statinsHigh correlation
dm patient medical history is highly correlated with study and 1 other fieldsHigh correlation
numbness autonomic symptoms is highly correlated with neuropathy autonomic symptomsHigh correlation
stroke patient medical history is highly correlated with strokeHigh correlation
insulin(yes or no) is highly correlated with completed visit statusHigh correlation
antiparkinsonian is highly correlated with completed visit statusHigh correlation
statins is highly correlated with hyperlipidemia patient medical historyHigh correlation
stroke is highly correlated with stroke patient medical historyHigh correlation
completed visit status is highly correlated with study and 2 other fieldsHigh correlation
oral hypoglycemic is highly correlated with study and 1 other fieldsHigh correlation
years is highly correlated with alcohol dose/week and 3 other fieldsHigh correlation
neuropathy autonomic symptoms is highly correlated with numbness autonomic symptomsHigh correlation
hyperlipidemia patient medical history is highly correlated with statinsHigh correlation
triglycmg/dl is highly correlated with chol/hd ratioHigh correlation
alcohol dose/week is highly correlated with years and 1 other fieldsHigh correlation
stroke patient medical history is highly correlated with strokeHigh correlation
glucose mg/dl is highly correlated with oral hypoglycemic and 4 other fieldsHigh correlation
previous tobacco use is highly correlated with years and 1 other fieldsHigh correlation
oral hypoglycemic is highly correlated with glucose mg/dl and 3 other fieldsHigh correlation
chol/hd ratio is highly correlated with triglycmg/dl and 1 other fieldsHigh correlation
statins is highly correlated with hyperlipidemia patient medical historyHigh correlation
current tobacco use is highly correlated with yearsHigh correlation
study is highly correlated with glucose mg/dl and 3 other fieldsHigh correlation
numbness autonomic symptoms is highly correlated with neuropathy autonomic symptomsHigh correlation
dm patient medical history is highly correlated with glucose mg/dl and 4 other fieldsHigh correlation
bmi is highly correlated with glucose mg/dl and 2 other fieldsHigh correlation
antihyperlipidemic is highly correlated with dm patient medical historyHigh correlation
stroke is highly correlated with stroke patient medical history and 3 other fieldsHigh correlation
hdl mg/dl is highly correlated with chol/hd ratio and 1 other fieldsHigh correlation
plt ct k/ul is highly correlated with glucose mg/dl and 1 other fieldsHigh correlation
height/m is highly correlated with genderHigh correlation
mass/kg is highly correlated with bmi and 1 other fieldsHigh correlation
pack years is highly correlated with years and 2 other fieldsHigh correlation
gender is highly correlated with hdl mg/dl and 2 other fieldsHigh correlation
years has 13 (5.7%) missing values Missing
atrial fibtrillation patient medical history has 3 (1.3%) missing values Missing
triglycmg/dl has 22 (9.7%) missing values Missing
alcohol dose/week has 16 (7.0%) missing values Missing
glucose mg/dl has 19 (8.4%) missing values Missing
chol/hd ratio has 14 (6.2%) missing values Missing
current tobacco use has 9 (4.0%) missing values Missing
crp (mg/l) has 45 (19.8%) missing values Missing
oh autonomic symptoms has 3 (1.3%) missing values Missing
cholestmg/dl has 23 (10.1%) missing values Missing
hdl mg/dl has 14 (6.2%) missing values Missing
plt ct k/ul has 19 (8.4%) missing values Missing
pack years has 18 (7.9%) missing values Missing
completed visit status has 80 (35.2%) missing values Missing
patient id is uniformly distributed Uniform
patient id has unique values Unique
years has 92 (40.5%) zeros Zeros
alcohol dose/week has 82 (36.1%) zeros Zeros
cancer# family history has 100 (44.1%) zeros Zeros
pack years has 94 (41.4%) zeros Zeros

Reproduction

Analysis started2022-03-15 09:43:58.058242
Analysis finished2022-03-15 09:45:38.574671
Duration1 minute and 40.52 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Distinct2
Distinct (%)0.9%
Missing2
Missing (%)0.9%
Memory size582.0 B
False
197 
True
28 
(Missing)
 
2
ValueCountFrequency (%)
False197
86.8%
True28
 
12.3%
(Missing)2
 
0.9%
2022-03-15T10:45:38.711672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct37
Distinct (%)17.3%
Missing13
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean14.8271028
Minimum0
Maximum64
Zeros92
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:39.005950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q327.75
95-th percentile48
Maximum64
Range64
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation17.113348
Coefficient of variation (CV)1.154193657
Kurtosis-0.2302921014
Mean14.8271028
Median Absolute Deviation (MAD)10
Skewness0.903433219
Sum3173
Variance292.8666798
MonotonicityNot monotonic
2022-03-15T10:45:39.407029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
092
40.5%
3017
 
7.5%
2013
 
5.7%
4013
 
5.7%
1010
 
4.4%
159
 
4.0%
256
 
2.6%
126
 
2.6%
244
 
1.8%
603
 
1.3%
Other values (27)41
18.1%
(Missing)13
 
5.7%
ValueCountFrequency (%)
092
40.5%
12
 
0.9%
22
 
0.9%
31
 
0.4%
42
 
0.9%
52
 
0.9%
71
 
0.4%
81
 
0.4%
92
 
0.9%
1010
 
4.4%
ValueCountFrequency (%)
641
 
0.4%
603
1.3%
572
0.9%
551
 
0.4%
521
 
0.4%
502
0.9%
482
0.9%
451
 
0.4%
432
0.9%
421
 
0.4%

neuropathy autonomic symptoms
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing1
Missing (%)0.4%
Memory size582.0 B
False
177 
True
49 
(Missing)
 
1
ValueCountFrequency (%)
False177
78.0%
True49
 
21.6%
(Missing)1
 
0.4%
2022-03-15T10:45:39.639197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing3
Missing (%)1.3%
Memory size582.0 B
False
215 
True
 
9
(Missing)
 
3
ValueCountFrequency (%)
False215
94.7%
True9
 
4.0%
(Missing)3
 
1.3%
2022-03-15T10:45:39.776202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

hyperlipidemia patient medical history
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing2
Missing (%)0.9%
Memory size582.0 B
True
116 
False
109 
(Missing)
 
2
ValueCountFrequency (%)
True116
51.1%
False109
48.0%
(Missing)2
 
0.9%
2022-03-15T10:45:39.877802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
193 
True
34 
ValueCountFrequency (%)
False193
85.0%
True34
 
15.0%
2022-03-15T10:45:39.969906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing1
Missing (%)0.4%
Memory size582.0 B
False
188 
True
38 
(Missing)
 
1
ValueCountFrequency (%)
False188
82.8%
True38
 
16.7%
(Missing)1
 
0.4%
2022-03-15T10:45:40.017121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

triglycmg/dl
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct139
Distinct (%)67.8%
Missing22
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean145.518361
Minimum0.264
Maximum473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:40.149345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.264
5-th percentile53.2
Q193
median135
Q3182
95-th percentile304.4
Maximum473
Range472.736
Interquartile range (IQR)89

Descriptive statistics

Standard deviation77.87132507
Coefficient of variation (CV)0.5351305811
Kurtosis2.80157899
Mean145.518361
Median Absolute Deviation (MAD)43
Skewness1.424182255
Sum29831.264
Variance6063.943268
MonotonicityNot monotonic
2022-03-15T10:45:40.558953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1365
 
2.2%
975
 
2.2%
1254
 
1.8%
1524
 
1.8%
953
 
1.3%
663
 
1.3%
1503
 
1.3%
1823
 
1.3%
783
 
1.3%
1453
 
1.3%
Other values (129)169
74.4%
(Missing)22
 
9.7%
ValueCountFrequency (%)
0.2641
0.4%
372
0.9%
431
0.4%
441
0.4%
461
0.4%
471
0.4%
501
0.4%
521
0.4%
532
0.9%
541
0.4%
ValueCountFrequency (%)
4731
0.4%
4421
0.4%
4241
0.4%
3721
0.4%
3671
0.4%
3621
0.4%
3211
0.4%
3201
0.4%
3151
0.4%
3131
0.4%

arbs
Boolean

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
212 
True
 
15
ValueCountFrequency (%)
False212
93.4%
True15
 
6.6%
2022-03-15T10:45:40.805239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

antiparkinsonian
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
225 
True
 
2
ValueCountFrequency (%)
False225
99.1%
True2
 
0.9%
2022-03-15T10:45:40.899239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

alcohol dose/week
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct28
Distinct (%)13.3%
Missing16
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean5.003554502
Minimum0
Maximum70
Zeros82
Zeros (%)36.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:41.111173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile22.5
Maximum70
Range70
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.94350967
Coefficient of variation (CV)2.187147091
Kurtosis18.69313131
Mean5.003554502
Median Absolute Deviation (MAD)1
Skewness4.042882042
Sum1055.75
Variance119.760404
MonotonicityNot monotonic
2022-03-15T10:45:41.455594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
082
36.1%
220
 
8.8%
120
 
8.8%
719
 
8.4%
317
 
7.5%
57
 
3.1%
46
 
2.6%
0.55
 
2.2%
144
 
1.8%
103
 
1.3%
Other values (18)28
 
12.3%
(Missing)16
 
7.0%
ValueCountFrequency (%)
082
36.1%
0.253
 
1.3%
0.55
 
2.2%
120
 
8.8%
1.51
 
0.4%
220
 
8.8%
2.51
 
0.4%
317
 
7.5%
46
 
2.6%
57
 
3.1%
ValueCountFrequency (%)
703
1.3%
491
 
0.4%
422
0.9%
352
0.9%
281
 
0.4%
251
 
0.4%
241
 
0.4%
213
1.3%
202
0.9%
151
 
0.4%

stroke patient medical history
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)1.3%
Missing2
Missing (%)0.9%
Memory size1.9 KiB
no
158 
yes
66 
tia
 
1

Length

Max length3
Median length2
Mean length2.297777778
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no158
69.6%
yes66
29.1%
tia1
 
0.4%
(Missing)2
 
0.9%

Length

2022-03-15T10:45:41.849311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:41.996210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no158
70.2%
yes66
29.3%
tia1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glucose mg/dl
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct80
Distinct (%)38.5%
Missing19
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean93.5
Minimum40
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:42.134761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60.35
Q175
median83
Q398
95-th percentile166.2
Maximum258
Range218
Interquartile range (IQR)23

Descriptive statistics

Standard deviation35.98402759
Coefficient of variation (CV)0.38485591
Kurtosis7.408407656
Mean93.5
Median Absolute Deviation (MAD)12
Skewness2.539727114
Sum19448
Variance1294.850242
MonotonicityNot monotonic
2022-03-15T10:45:43.411358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
818
 
3.5%
917
 
3.1%
767
 
3.1%
807
 
3.1%
957
 
3.1%
837
 
3.1%
787
 
3.1%
726
 
2.6%
796
 
2.6%
776
 
2.6%
Other values (70)140
61.7%
(Missing)19
 
8.4%
ValueCountFrequency (%)
401
 
0.4%
511
 
0.4%
521
 
0.4%
531
 
0.4%
542
0.9%
571
 
0.4%
583
1.3%
601
 
0.4%
612
0.9%
633
1.3%
ValueCountFrequency (%)
2581
0.4%
2531
0.4%
2521
0.4%
2311
0.4%
2221
0.4%
2131
0.4%
2111
0.4%
2071
0.4%
1901
0.4%
1791
0.4%
Distinct5
Distinct (%)2.2%
Missing2
Missing (%)0.9%
Memory size1.9 KiB
0.0
105 
1.0
70 
2.0
34 
3.0
13 
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0105
46.3%
1.070
30.8%
2.034
 
15.0%
3.013
 
5.7%
4.03
 
1.3%
(Missing)2
 
0.9%

Length

2022-03-15T10:45:43.759923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:43.945870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0105
46.7%
1.070
31.1%
2.034
 
15.1%
3.013
 
5.8%
4.03
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

previous tobacco use
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing2
Missing (%)0.9%
Memory size582.0 B
True
127 
False
98 
(Missing)
 
2
ValueCountFrequency (%)
True127
55.9%
False98
43.2%
(Missing)2
 
0.9%
2022-03-15T10:45:44.010179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0.0
133 
1.0
74 
2.0
17 
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0133
58.6%
1.074
32.6%
2.017
 
7.5%
3.03
 
1.3%

Length

2022-03-15T10:45:44.134714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:44.312193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0133
58.6%
1.074
32.6%
2.017
 
7.5%
3.03
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
218 
True
 
9
ValueCountFrequency (%)
False218
96.0%
True9
 
4.0%
2022-03-15T10:45:44.441158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

oral hypoglycemic
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
170 
True
57 
ValueCountFrequency (%)
False170
74.9%
True57
 
25.1%
2022-03-15T10:45:44.534746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

estrogen
Boolean

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
222 
True
 
5
ValueCountFrequency (%)
False222
97.8%
True5
 
2.2%
2022-03-15T10:45:44.629508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct5
Distinct (%)2.2%
Missing1
Missing (%)0.4%
Memory size1.9 KiB
1.0
90 
0.0
76 
2.0
40 
3.0
17 
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.090
39.6%
0.076
33.5%
2.040
17.6%
3.017
 
7.5%
4.03
 
1.3%
(Missing)1
 
0.4%

Length

2022-03-15T10:45:44.844141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:45.041390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.090
39.8%
0.076
33.6%
2.040
17.7%
3.017
 
7.5%
4.03
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
True
165 
False
62 
ValueCountFrequency (%)
True165
72.7%
False62
 
27.3%
2022-03-15T10:45:45.178138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

chol/hd ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)18.3%
Missing14
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean3.324413146
Minimum1.8
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:45.410986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q12.7
median3.2
Q33.9
95-th percentile4.9
Maximum7.6
Range5.8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.925816956
Coefficient of variation (CV)0.2784903427
Kurtosis2.149611001
Mean3.324413146
Median Absolute Deviation (MAD)0.6
Skewness1.169267309
Sum708.1
Variance0.8571370361
MonotonicityNot monotonic
2022-03-15T10:45:45.791855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3.315
 
6.6%
2.714
 
6.2%
2.412
 
5.3%
2.912
 
5.3%
3.211
 
4.8%
3.111
 
4.8%
2.610
 
4.4%
310
 
4.4%
3.49
 
4.0%
2.39
 
4.0%
Other values (29)100
44.1%
(Missing)14
 
6.2%
ValueCountFrequency (%)
1.81
 
0.4%
1.93
 
1.3%
22
 
0.9%
2.14
 
1.8%
2.25
 
2.2%
2.39
4.0%
2.412
5.3%
2.54
 
1.8%
2.610
4.4%
2.714
6.2%
ValueCountFrequency (%)
7.61
 
0.4%
6.41
 
0.4%
6.11
 
0.4%
61
 
0.4%
5.82
0.9%
5.31
 
0.4%
5.12
0.9%
4.93
1.3%
4.82
0.9%
4.71
 
0.4%

statins
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
125 
True
102 
ValueCountFrequency (%)
False125
55.1%
True102
44.9%
2022-03-15T10:45:45.979379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

patient id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
s0250
 
1
s0298
 
1
s0231
 
1
s0232
 
1
s0233
 
1
Other values (222)
222 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st rows0250
2nd rows0254
3rd rows0255
4th rows0256
5th rows0257

Common Values

ValueCountFrequency (%)
s02501
 
0.4%
s02981
 
0.4%
s02311
 
0.4%
s02321
 
0.4%
s02331
 
0.4%
s02351
 
0.4%
s02361
 
0.4%
s02371
 
0.4%
s02391
 
0.4%
s02401
 
0.4%
Other values (217)217
95.6%

Length

2022-03-15T10:45:46.064980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s02501
 
0.4%
s04031
 
0.4%
s02811
 
0.4%
s02551
 
0.4%
s02561
 
0.4%
s02571
 
0.4%
s02621
 
0.4%
s02641
 
0.4%
s02671
 
0.4%
s02701
 
0.4%
Other values (217)217
95.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
170 
True
57 
ValueCountFrequency (%)
False170
74.9%
True57
 
25.1%
2022-03-15T10:45:46.152110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

diuretics
Boolean

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
180 
True
47 
ValueCountFrequency (%)
False180
79.3%
True47
 
20.7%
2022-03-15T10:45:46.205232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
206 
True
21 
ValueCountFrequency (%)
False206
90.7%
True21
 
9.3%
2022-03-15T10:45:46.287017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

current tobacco use
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.9%
Missing9
Missing (%)4.0%
Memory size582.0 B
False
194 
True
24 
(Missing)
 
9
ValueCountFrequency (%)
False194
85.5%
True24
 
10.6%
(Missing)9
 
4.0%
2022-03-15T10:45:46.393715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
165 
True
62 
ValueCountFrequency (%)
False165
72.7%
True62
 
27.3%
2022-03-15T10:45:46.511645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

crp (mg/l)
Real number (ℝ≥0)

MISSING

Distinct141
Distinct (%)77.5%
Missing45
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean2.725313187
Minimum0.028
Maximum59.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:46.758037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.028
5-th percentile0.0846
Q10.345
median0.9
Q32.035
95-th percentile9.658
Maximum59.3
Range59.272
Interquartile range (IQR)1.69

Descriptive statistics

Standard deviation6.592582472
Coefficient of variation (CV)2.4190183
Kurtosis37.81760584
Mean2.725313187
Median Absolute Deviation (MAD)0.7
Skewness5.599907972
Sum496.007
Variance43.46214365
MonotonicityNot monotonic
2022-03-15T10:45:47.165274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.45
 
2.2%
0.614
 
1.8%
0.284
 
1.8%
0.454
 
1.8%
0.853
 
1.3%
1.823
 
1.3%
1.33
 
1.3%
0.342
 
0.9%
2.22
 
0.9%
0.232
 
0.9%
Other values (131)150
66.1%
(Missing)45
 
19.8%
ValueCountFrequency (%)
0.0281
0.4%
0.0321
0.4%
0.0381
0.4%
0.0471
0.4%
0.051
0.4%
0.061
0.4%
0.0612
0.9%
0.0821
0.4%
0.0841
0.4%
0.0961
0.4%
ValueCountFrequency (%)
59.31
0.4%
40.61
0.4%
30.21
0.4%
26.61
0.4%
24.31
0.4%
16.91
0.4%
15.91
0.4%
13.61
0.4%
11.61
0.4%
9.671
0.4%

study
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
cerebral_elderly_stroke
147 
cerebral_perfusion_diabetes
80 

Length

Max length27
Median length23
Mean length24.40969163
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcerebral_perfusion_diabetes
2nd rowcerebral_perfusion_diabetes
3rd rowcerebral_perfusion_diabetes
4th rowcerebral_perfusion_diabetes
5th rowcerebral_perfusion_diabetes

Common Values

ValueCountFrequency (%)
cerebral_elderly_stroke147
64.8%
cerebral_perfusion_diabetes80
35.2%

Length

2022-03-15T10:45:47.577398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:47.851832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cerebral_elderly_stroke147
64.8%
cerebral_perfusion_diabetes80
35.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numbness autonomic symptoms
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing2
Missing (%)0.9%
Memory size582.0 B
False
173 
True
52 
(Missing)
 
2
ValueCountFrequency (%)
False173
76.2%
True52
 
22.9%
(Missing)2
 
0.9%
2022-03-15T10:45:47.979649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

oh autonomic symptoms
Boolean

MISSING

Distinct2
Distinct (%)0.9%
Missing3
Missing (%)1.3%
Memory size582.0 B
False
176 
True
48 
(Missing)
 
3
ValueCountFrequency (%)
False176
77.5%
True48
 
21.1%
(Missing)3
 
1.3%
2022-03-15T10:45:48.085621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

insulin(yes or no)
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
217 
True
 
10
ValueCountFrequency (%)
False217
95.6%
True10
 
4.4%
2022-03-15T10:45:48.209709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

dm patient medical history
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing2
Missing (%)0.9%
Memory size582.0 B
False
154 
True
71 
(Missing)
 
2
ValueCountFrequency (%)
False154
67.8%
True71
31.3%
(Missing)2
 
0.9%
2022-03-15T10:45:48.403510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
119 
True
108 
ValueCountFrequency (%)
False119
52.4%
True108
47.6%
2022-03-15T10:45:48.573876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

bmi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct222
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.80202659
Minimum18.24688209
Maximum45.7676239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:49.089536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.24688209
5-th percentile21.07968757
Q124.32819341
median26.7550539
Q330.09179293
95-th percentile38.59556148
Maximum45.7676239
Range27.52074181
Interquartile range (IQR)5.763599517

Descriptive statistics

Standard deviation5.202748673
Coefficient of variation (CV)0.1871355909
Kurtosis0.5647240078
Mean27.80202659
Median Absolute Deviation (MAD)2.6148286
Skewness0.8730347813
Sum6311.060036
Variance27.06859375
MonotonicityNot monotonic
2022-03-15T10:45:49.614100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.392209913
 
1.3%
23.493080182
 
0.9%
21.079687572
 
0.9%
24.210384972
 
0.9%
25.465380381
 
0.4%
27.115631171
 
0.4%
24.030698521
 
0.4%
18.951084681
 
0.4%
36.15786411
 
0.4%
35.275117321
 
0.4%
Other values (212)212
93.4%
ValueCountFrequency (%)
18.246882091
0.4%
18.637843611
0.4%
18.951084681
0.4%
19.107705221
0.4%
19.529710551
0.4%
19.633714881
0.4%
19.827735461
0.4%
19.90931261
0.4%
20.83265311
0.4%
20.985317911
0.4%
ValueCountFrequency (%)
45.76762391
0.4%
43.587750141
0.4%
42.855727311
0.4%
40.33950621
0.4%
40.25567571
0.4%
39.659799171
0.4%
39.636127361
0.4%
39.00209321
0.4%
38.959417271
0.4%
38.906251
0.4%

age
Real number (ℝ≥0)

Distinct32
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.22026432
Minimum50
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:49.899737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52
Q161
median67
Q372
95-th percentile79
Maximum83
Range33
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.195883594
Coefficient of variation (CV)0.1237670021
Kurtosis-0.7690168194
Mean66.22026432
Median Absolute Deviation (MAD)6
Skewness-0.1999095068
Sum15032
Variance67.17250789
MonotonicityNot monotonic
2022-03-15T10:45:50.151109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6717
 
7.5%
7114
 
6.2%
7013
 
5.7%
7812
 
5.3%
6612
 
5.3%
7412
 
5.3%
6212
 
5.3%
6411
 
4.8%
6510
 
4.4%
728
 
3.5%
Other values (22)106
46.7%
ValueCountFrequency (%)
505
2.2%
515
2.2%
525
2.2%
537
3.1%
547
3.1%
555
2.2%
573
1.3%
587
3.1%
595
2.2%
606
2.6%
ValueCountFrequency (%)
831
 
0.4%
811
 
0.4%
807
3.1%
794
 
1.8%
7812
5.3%
774
 
1.8%
764
 
1.8%
753
 
1.3%
7412
5.3%
736
2.6%
Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0.0
125 
1.0
59 
2.0
31 
3.0
 
10
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row3.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125
55.1%
1.059
26.0%
2.031
 
13.7%
3.010
 
4.4%
4.02
 
0.9%

Length

2022-03-15T10:45:50.519393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:50.662831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0125
55.1%
1.059
26.0%
2.031
 
13.7%
3.010
 
4.4%
4.02
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

antihyperlipidemic
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
186 
True
41 
ValueCountFrequency (%)
False186
81.9%
True41
 
18.1%
2022-03-15T10:45:50.747320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
156 
True
71 
ValueCountFrequency (%)
False156
68.7%
True71
31.3%
2022-03-15T10:45:50.867238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

stroke
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size355.0 B
False
158 
True
69 
ValueCountFrequency (%)
False158
69.6%
True69
30.4%
2022-03-15T10:45:50.920825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

cancer# family history
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)2.7%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.8274336283
Minimum0
Maximum5
Zeros100
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:51.016270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9292049445
Coefficient of variation (CV)1.12299635
Kurtosis1.594153313
Mean0.8274336283
Median Absolute Deviation (MAD)1
Skewness1.18899985
Sum187
Variance0.8634218289
MonotonicityNot monotonic
2022-03-15T10:45:51.167546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0100
44.1%
181
35.7%
232
 
14.1%
311
 
4.8%
41
 
0.4%
51
 
0.4%
(Missing)1
 
0.4%
ValueCountFrequency (%)
0100
44.1%
181
35.7%
232
 
14.1%
311
 
4.8%
41
 
0.4%
51
 
0.4%
ValueCountFrequency (%)
51
 
0.4%
41
 
0.4%
311
 
4.8%
232
 
14.1%
181
35.7%
0100
44.1%

cholestmg/dl
Real number (ℝ≥0)

MISSING

Distinct106
Distinct (%)52.0%
Missing23
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean183.4558824
Minimum95
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:51.424780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile124
Q1159
median180
Q3208
95-th percentile253.7
Maximum314
Range219
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.38543205
Coefficient of variation (CV)0.2092352208
Kurtosis0.2631377911
Mean183.4558824
Median Absolute Deviation (MAD)24
Skewness0.3764529077
Sum37425
Variance1473.441394
MonotonicityNot monotonic
2022-03-15T10:45:51.766039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2256
 
2.6%
1805
 
2.2%
1655
 
2.2%
1605
 
2.2%
1855
 
2.2%
1414
 
1.8%
1584
 
1.8%
1744
 
1.8%
2204
 
1.8%
1614
 
1.8%
Other values (96)158
69.6%
(Missing)23
 
10.1%
ValueCountFrequency (%)
951
0.4%
981
0.4%
1001
0.4%
1061
0.4%
1091
0.4%
1131
0.4%
1181
0.4%
1191
0.4%
1211
0.4%
1221
0.4%
ValueCountFrequency (%)
3141
0.4%
2831
0.4%
2712
0.9%
2701
0.4%
2681
0.4%
2651
0.4%
2641
0.4%
2581
0.4%
2551
0.4%
2541
0.4%

hdl mg/dl
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct62
Distinct (%)29.1%
Missing14
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean58.21150235
Minimum0.52
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:52.014438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile35.6
Q148
median56
Q368
95-th percentile88.4
Maximum106
Range105.48
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.88909762
Coefficient of variation (CV)0.2901333403
Kurtosis0.717767339
Mean58.21150235
Median Absolute Deviation (MAD)10
Skewness0.1587854112
Sum12399.05
Variance285.2416185
MonotonicityNot monotonic
2022-03-15T10:45:52.285794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5111
 
4.8%
538
 
3.5%
498
 
3.5%
607
 
3.1%
486
 
2.6%
566
 
2.6%
616
 
2.6%
415
 
2.2%
525
 
2.2%
815
 
2.2%
Other values (52)146
64.3%
(Missing)14
 
6.2%
ValueCountFrequency (%)
0.521
 
0.4%
0.531
 
0.4%
283
1.3%
321
 
0.4%
334
1.8%
351
 
0.4%
363
1.3%
374
1.8%
383
1.3%
392
0.9%
ValueCountFrequency (%)
1062
0.9%
982
0.9%
942
0.9%
921
 
0.4%
911
 
0.4%
893
1.3%
882
0.9%
861
 
0.4%
843
1.3%
832
0.9%

plt ct k/ul
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct144
Distinct (%)69.2%
Missing19
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean266.6923077
Minimum110
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:52.545369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile158.4
Q1224
median259.5
Q3309.25
95-th percentile399.5
Maximum516
Range406
Interquartile range (IQR)85.25

Descriptive statistics

Standard deviation71.79507565
Coefficient of variation (CV)0.2692056485
Kurtosis0.6214580341
Mean266.6923077
Median Absolute Deviation (MAD)44
Skewness0.5427802412
Sum55472
Variance5154.532887
MonotonicityNot monotonic
2022-03-15T10:45:52.818547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2595
 
2.2%
2355
 
2.2%
2504
 
1.8%
3004
 
1.8%
2454
 
1.8%
2473
 
1.3%
2553
 
1.3%
2813
 
1.3%
2443
 
1.3%
2693
 
1.3%
Other values (134)171
75.3%
(Missing)19
 
8.4%
ValueCountFrequency (%)
1101
0.4%
1141
0.4%
1231
0.4%
1281
0.4%
1351
0.4%
1431
0.4%
1481
0.4%
1501
0.4%
1521
0.4%
1551
0.4%
ValueCountFrequency (%)
5161
0.4%
4811
0.4%
4701
0.4%
4391
0.4%
4361
0.4%
4301
0.4%
4231
0.4%
4161
0.4%
4131
0.4%
4111
0.4%

height/m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.670556828
Minimum1.46
Maximum1.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:53.065942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.46
5-th percentile1.52
Q11.6
median1.6764
Q31.74
95-th percentile1.82
Maximum1.92
Range0.46
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.09511245782
Coefficient of variation (CV)0.05693458385
Kurtosis-0.6301470024
Mean1.670556828
Median Absolute Deviation (MAD)0.0736
Skewness0.02873237246
Sum379.2164
Variance0.009046379632
MonotonicityNot monotonic
2022-03-15T10:45:53.373144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.701813
 
5.7%
1.6812
 
5.3%
1.69
 
4.0%
1.578
 
3.5%
1.747
 
3.1%
1.727
 
3.1%
1.67647
 
3.1%
1.636
 
2.6%
1.62566
 
2.6%
1.80346
 
2.6%
Other values (62)146
64.3%
ValueCountFrequency (%)
1.461
 
0.4%
1.472
 
0.9%
1.47321
 
0.4%
1.491
 
0.4%
1.49861
 
0.4%
1.51
 
0.4%
1.5021
 
0.4%
1.526
2.6%
1.5243
1.3%
1.531
 
0.4%
ValueCountFrequency (%)
1.921
 
0.4%
1.91
 
0.4%
1.85421
 
0.4%
1.852
0.9%
1.841
 
0.4%
1.833
1.3%
1.82882
0.9%
1.823
1.3%
1.8181
 
0.4%
1.811
 
0.4%

mass/kg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct183
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.81583642
Minimum45.81
Maximum133.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:53.745418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum45.81
5-th percentile54.43032532
Q165.5
median75.15
Q386.925
95-th percentile113.0836647
Maximum133.05
Range87.24
Interquartile range (IQR)21.425

Descriptive statistics

Standard deviation17.0374199
Coefficient of variation (CV)0.218945406
Kurtosis0.8512937935
Mean77.81583642
Median Absolute Deviation (MAD)10.5
Skewness0.8652911802
Sum17664.19487
Variance290.2736768
MonotonicityNot monotonic
2022-03-15T10:45:53.999310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.03885554
 
1.8%
72.57477924
 
1.8%
63.50293184
 
1.8%
64.410116543
 
1.3%
95.25439773
 
1.3%
71.667594463
 
1.3%
53.977492033
 
1.3%
54.884676773
 
1.3%
77.11070293
 
1.3%
69.853
 
1.3%
Other values (173)194
85.5%
ValueCountFrequency (%)
45.811
 
0.4%
47.171
 
0.4%
48.991
 
0.4%
49.891
 
0.4%
51.51
 
0.4%
52.163122552
0.9%
52.251
 
0.4%
53.977492033
1.3%
54.431
 
0.4%
54.43108441
 
0.4%
ValueCountFrequency (%)
133.051
0.4%
130.71
0.4%
129.751
0.4%
128.951
0.4%
1241
0.4%
122.51
0.4%
121.61
0.4%
119.41
0.4%
115.91
0.4%
114.651
0.4%

pack years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct66
Distinct (%)31.6%
Missing18
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean16.37165885
Minimum0
Maximum160
Zeros94
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-03-15T10:45:54.251933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.14
Q326
95-th percentile65.2
Maximum160
Range160
Interquartile range (IQR)26

Descriptive statistics

Standard deviation25.10450895
Coefficient of variation (CV)1.53341266
Kurtosis5.552543764
Mean16.37165885
Median Absolute Deviation (MAD)2.14
Skewness2.076046542
Sum3421.6767
Variance630.2363695
MonotonicityNot monotonic
2022-03-15T10:45:54.677276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
094
41.4%
107
 
3.1%
157
 
3.1%
607
 
3.1%
307
 
3.1%
206
 
2.6%
404
 
1.8%
124
 
1.8%
804
 
1.8%
253
 
1.3%
Other values (56)66
29.1%
(Missing)18
 
7.9%
ValueCountFrequency (%)
094
41.4%
0.141
 
0.4%
0.16671
 
0.4%
0.51
 
0.4%
0.61
 
0.4%
12
 
0.9%
1.141
 
0.4%
1.251
 
0.4%
1.431
 
0.4%
1.711
 
0.4%
ValueCountFrequency (%)
1601
 
0.4%
1001
 
0.4%
961
 
0.4%
861
 
0.4%
804
1.8%
721
 
0.4%
701
 
0.4%
661
 
0.4%
641
 
0.4%
62.51
 
0.4%

gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
female
118 
male
109 

Length

Max length6
Median length6
Mean length5.039647577
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female118
52.0%
male109
48.0%

Length

2022-03-15T10:45:55.066300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:55.335115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
female118
52.0%
male109
48.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

completed visit status
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)3.4%
Missing80
Missing (%)35.2%
Memory size1.9 KiB
completed
88 
excluded
43 
ineligible
 
8
lost to followup
 
6
v1
 
2

Length

Max length16
Median length9
Mean length8.952380952
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowineligible
2nd rowcompleted
3rd rowexcluded
4th rowcompleted
5th rowcompleted

Common Values

ValueCountFrequency (%)
completed88
38.8%
excluded43
18.9%
ineligible8
 
3.5%
lost to followup6
 
2.6%
v12
 
0.9%
(Missing)80
35.2%

Length

2022-03-15T10:45:55.646519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T10:45:55.823108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
completed88
55.3%
excluded43
27.0%
ineligible8
 
5.0%
lost6
 
3.8%
to6
 
3.8%
followup6
 
3.8%
v12
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-15T10:45:22.790804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:30.068901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:36.384366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:39.410038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:42.523192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:45.547179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:49.035939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:52.351851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:56.139053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.363167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:03.266004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.803344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:08.934694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:11.956964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:16.531667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:23.407312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:30.833235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:36.586175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:39.605111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:42.727069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:45.782033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:49.232071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:52.572675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:56.619016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.536641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:03.491935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.934091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:09.187640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:12.123827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:16.876684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:23.991204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:31.022899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:36.773549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:39.825877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:42.927447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:45.961568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:49.442211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:52.771595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:56.986328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.687979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:03.722087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.084853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:09.391798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:12.295316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:17.161710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:24.550795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:33.944415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:37.003392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:40.018851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:43.133048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:46.141801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:49.642802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:53.007731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:57.323032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.868550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:03.902376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.279269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:09.584367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:12.437789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:17.473680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:24.949545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:34.074715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:37.173480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:40.190132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:43.332930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:46.359515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:49.825727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:53.212694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:57.696120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:01.079828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.022156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.437299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:09.761841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:12.674829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:17.864599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:25.261268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:34.208078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:37.396302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:40.416081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:43.536254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:46.576308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:50.016896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:53.425118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:58.069557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:01.324596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.187650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.624810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.006489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:12.855605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:18.279057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:25.876059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:34.437910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:37.609113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:40.610340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:43.745547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:46.794683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:50.192550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:53.647908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:58.439212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:01.592825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.357416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.784648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.156778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:13.195152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:18.807124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:26.077803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:34.623598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:37.820210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:40.823031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:43.940613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:47.019396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:50.397696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:53.860120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:58.938247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:01.778204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.526653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:06.914027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.336344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:13.558245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:19.285224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:26.601765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:34.838475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:38.006799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:41.049018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:44.070860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:47.207218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:50.600489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:54.029386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:59.145558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:01.963436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.678532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:07.081099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.527573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:13.996403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:19.838793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:27.065828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:35.067354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:38.175113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:41.271319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:44.257924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:47.410221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:50.800848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:54.207634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:59.332827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.142895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.802001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:07.777998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.706472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:14.200042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:20.170525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:27.419091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:35.284118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:38.374153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:41.477338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:44.460534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:47.621206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:51.003399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:54.395282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:59.501108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.285284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:04.964083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:07.938118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:10.925620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:14.545981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:20.516305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:27.727866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:35.520663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:38.594310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:41.708954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:44.673449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:47.830212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:51.350898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:54.674801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:59.669766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.457047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.138317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:08.141578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:11.130708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:14.853350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:20.939441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:27.986122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:35.776417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:38.821177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:41.917551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:44.896742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:48.017658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:51.650072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:55.147260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:59.880600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.649051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.320220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:08.338475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:11.393807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:15.312845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:21.449462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:28.164518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:35.961739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:39.022988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:42.096425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:45.116355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:48.635427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:51.956981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:55.387972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.068675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.815263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.483556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:08.512336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:11.589431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:15.728467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:21.866399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:28.464146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:36.138139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:39.223325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:42.283150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:45.314148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:48.831638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:52.124575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:44:55.720281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:00.200350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:02.982681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:05.651932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:08.677909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:11.757643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:16.058836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T10:45:22.227373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-15T10:45:56.014025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-15T10:45:56.675769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-15T10:45:57.483407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-15T10:45:57.924807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-15T10:45:58.567675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-15T10:45:29.428961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-15T10:45:34.396659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-15T10:45:36.776291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-15T10:45:37.923755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

cancer patient medical historyyearsneuropathy autonomic symptomsatrial fibtrillation patient medical historyhyperlipidemia patient medical historyca ++ blockerssyncope autonomic symptomstriglycmg/dlarbsantiparkinsonianalcohol dose/weekstroke patient medical historyglucose mg/dlhtn family historyprevious tobacco usestrokefamily historyanticoagulantsoral hypoglycemicestrogenheartdisease family historyprevious alcohol usechol/hd ratiostatinspatient idbeta blockersdiureticspainful feet autonomic symptomscurrent tobacco useace inhibitorscrp (mg/l)studynumbness autonomic symptomsoh autonomic symptomsinsulin(yes or no)dm patient medical historyantiplateletsbmiagedm family historyantihyperlipidemicdizziness autonomic symptomsstrokecancer# family historycholestmg/dlhdl mg/dlplt ct k/ulheight/mmass/kgpack yearsgendercompleted visit status
0no10.0nonononono267.0nono7.00no211.01.0yes0.0noyesno1.0yes4.5nos0250nonononono0.240cerebral_perfusion_diabetesnononoyesno35.782279501.0nonono1.0135.036.0250.01.790114.652.86maleNaN
1no1.0nonononoyes95.0nono0.50no95.01.0yes1.0nonono1.0yes3.0nos0254nonononono0.104cerebral_perfusion_diabetesnonononono24.275148690.0nonono1.0176.059.0242.01.53156.900.14femaleNaN
2no0.0nonoyesnonoNaNnono9.00noNaN0.0no0.0nonono3.0yesNaNyess0255yesnonononoNaNcerebral_perfusion_diabetesnononoyesno28.466932783.0nonono0.0NaNNaNNaN1.52065.770.00femaleNaN
3yes20.0nonoyesnoNaN111.0nono3.00no111.00.0yes1.0noyesno1.0yes2.6yess0256nonononono0.060cerebral_perfusion_diabetesnononoyesyes29.629630653.0nonono2.0194.075.0315.01.57573.505.71femaleNaN
4no0.0nononononoNaNnono0.00noNaN1.0no1.0nonono1.0noNaNnos0257nononononoNaNcerebral_perfusion_diabetesnonononono24.328720550.0nonono0.0NaNNaNNaN1.70070.310.00maleNaN
5no17.0yesnoyesyesno186.0yesno0.00no98.01.0yes1.0nonono3.0yes4.5yess0262nononononoNaNcerebral_perfusion_diabetesyesnonoyesno37.093333673.0yesnono0.0178.040.0NaN1.50083.4634.00femaleNaN
6no20.0yesnoyesyesno185.0nonoNaNno159.01.0yes0.0nonono0.0no3.2yess0264noyesnonoyes0.061cerebral_perfusion_diabetesnonoyesyesyes33.184005661.0nonono1.0122.038.0181.01.72298.4020.00femaleNaN
7no20.0yesnoyesnono205.0nono7.00no104.02.0yes3.0noyesno0.0yes3.3yess0267yesnoyesnoyes0.061cerebral_perfusion_diabetesnononoyesyes30.215419742.0yesnono0.0162.049.0283.01.68085.2820.00maleNaN
8no20.0yesnoyesnono200.0noyes0.25no112.01.0yes1.0yesnono2.0yes3.4yess0270yesyesyesnoyes0.038cerebral_perfusion_diabetesyesnonoyesno32.129373811.0yesnono3.0135.040.0280.01.73096.1610.00maleNaN
9no15.0nonononono167.0nono1.00no84.00.0yes1.0nonono0.0yes2.9nos0271nonononono0.082cerebral_perfusion_diabetesnonononono25.393282572.0noyesno2.0217.076.0317.01.68071.6715.00femaleNaN

Last rows

cancer patient medical historyyearsneuropathy autonomic symptomsatrial fibtrillation patient medical historyhyperlipidemia patient medical historyca ++ blockerssyncope autonomic symptomstriglycmg/dlarbsantiparkinsonianalcohol dose/weekstroke patient medical historyglucose mg/dlhtn family historyprevious tobacco usestrokefamily historyanticoagulantsoral hypoglycemicestrogenheartdisease family historyprevious alcohol usechol/hd ratiostatinspatient idbeta blockersdiureticspainful feet autonomic symptomscurrent tobacco useace inhibitorscrp (mg/l)studynumbness autonomic symptomsoh autonomic symptomsinsulin(yes or no)dm patient medical historyantiplateletsbmiagedm family historyantihyperlipidemicdizziness autonomic symptomsstrokecancer# family historycholestmg/dlhdl mg/dlplt ct k/ulheight/mmass/kgpack yearsgendercompleted visit status
217no45.0nonoyesnono239.0nono0.0yes84.01.0yes1.0yesnono0.0yes3.8yess0388nononoyesyes1.70cerebral_elderly_strokenoyesnonoyes25.872580611.0noyesyes1.0184.049.0269.01.6267.906.75femalecompleted
218no24.0nonoyesnono270.0yesno1.0yes86.00.0yes0.0nonono2.0yes4.1nos0389yesyesnonono1.50cerebral_elderly_strokenonononoyes38.438094500.0yesnoyes1.0210.051.0324.01.67107.2024.00femalecompleted
219no0.0nonononono67.0nono0.0yes75.01.0no0.0nonono0.0no2.4yess0394nonononono2.00cerebral_elderly_strokenonononoyes39.636127590.0noyesyes1.0119.049.0175.01.71115.900.00femaleexcluded
220no15.0yesyesnoyesyes138.0nono7.0yesNaN3.0yes2.0yesnono1.0yes5.8nos0397nonononoyes3.90cerebral_elderly_strokeyesnononono27.083520740.0noyesyes0.0283.049.0284.01.8390.7015.00malecompleted
221no0.0nonononono44.0nono0.0no70.00.0no0.0nonono0.0yes1.9nos0399nonononono0.23cerebral_elderly_strokenonononono20.985318510.0nonono1.0180.094.0255.01.8168.750.00femalecompleted
222no20.0nononoyesno156.0nono6.0yes67.01.0yes0.0nonono0.0yes4.9nos0400noyesnonoyesNaNcerebral_elderly_strokenonononoyes29.019064533.0nonoyes1.0236.048.0416.01.7285.8520.00maleexcluded
223no4.0nonoyesnono240.0nono0.0yes70.00.0yes0.0nonono0.0no4.3yess0401nononononoNaNcerebral_elderly_strokeyesnononoyes31.462585640.0yesnoyes0.0230.053.0413.01.6888.800.60maleexcluded
224no30.0nonononono55.0nono4.0yes81.00.0yes1.0nonono0.0yes2.3nos0402nononoyesno0.43cerebral_elderly_strokeyesnononoyes29.117860540.0yesnoyes1.0137.060.0266.01.8296.45NaNmalecompleted
225noNaNnonoyesnono119.0nono0.0yes63.01.0yes1.0nonono1.0yes3.4yess0404nononononoNaNcerebral_elderly_strokeyesnononoyes26.297999610.0nonoyes0.0124.037.0161.01.7277.80NaNmaleexcluded
226no4.0yesnoyesnono118.0nono7.0yes93.01.0yes0.0nonoyes1.0yes2.9yess0414yesnononoyesNaNcerebral_elderly_strokeyesnononoyes21.353547630.0noyesyes0.0185.063.0327.01.7464.654.00femaleexcluded